Investigation of regions impacting inbreeding depression and their association with the additive genetic effect for United States and Australia Jersey dairy cattle

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Howard, Jeremy T ^a   Haile Mariam, Mekonnen ^b   Pryce, Jennie E ^b,c   Maltecca, Christian ^a  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b Department of Civil and Environmental Engineering   (Australia)

^c LA TROBE UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIVE GENETIC EFFECT; ARTICLE; AUSTRALIA; CATTLE FARMING; DAIRY CATTLE; EPISTASIS; FAT CONTENT; FEMALE; GENETIC PARAMETERS; HOMOZYGOSITY; INBREEDING DEPRESSION; MILK PRODUCTION; NONHUMAN; PROTEIN CONTENT; RUNS OF HOMOZYGOSITY; SINGLE NUCLEOTIDE POLYMORPHISM; UNITED STATES; ANIMAL; BOVINE; GENETICS; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; GENOTYPE; HUMAN; INBREEDING; LACTATION; MILK; PHENOTYPE; QUANTITATIVE TRAIT LOCUS;

ANIMALS; AUSTRALIA; CATTLE; FEMALE; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; GENOTYPE; HUMANS; INBREEDING; LACTATION; MILK; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI; UNITED STATES;

EID: 84945200087     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-015-2001-7     Document Type: Article

References (52)

1. Wright S. Coefficients of inbreeding and relationship. Am Nat. 1922;56:330-8.
2. Malcot G. Les Mathmatiques de l'Hérédité. Paris: Masson; 1948.
3. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;40:449-54.
4. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyhold DR, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565-9.
5. Toro MA, García-Cortés LA, Legarra A. A note on the rationale for estimating genealogical coancestry from molecular markers. Genet Sel Evol. 2011;43:27.
6. Keller MC, Visscher PM, Goddard ME. Quantification of inbreeding due to distant ancestors and its detection using dense single nucleotide polymorphism data. Genetics. 2011;189:237-49.
7. Falconer DS, Mackay TFS. Introduction to quantitative genetics. 4th ed. New York, NY: Longman Scientific and Technical; 1996.
8. Leroy G. Inbreeding depression in livestock species: review and meta-analysis. Anim Genet. 2014;45:618-28.
9. Misztal I, Lawlor TJ, Gengler N. Relationships among estimates of inbreeding depression, dominance and additive variance for linear traits in Holsteins. Genet Sel Evol. 1997;29:319-26.
10. Lacy RC, Alaks G, Walsh A. Hierarchical analysis of inbreeding depression in Peromyscus polionotus. Evolution. 1996;50:2187-200.
11. Gulisija D, Gianola D, Weigel KA, Toro MA. Between-founder heterogeneity in inbreeding depression for production in Jersey cows. Livest Sci. 2006;104:244-53.
12. Sun C, VanRaden PM, Cole JB, O'Connell JR. Improvement of prediction ability for genomic selection of dairy cattle by including dominance effects. PLoS One. 2014;9:e103934.
13. Ertl J, Legarra A, Vitezica ZG, Varona L, Edel C, Emmerling R, et al. Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle. Genet Sel Evol. 2014;46:40.
14. Saura M, Fernández A, Varona L, Fernández AI, de Cara MÁR, Barragán C, et al. Detecting inbreeding depression for reproductive traits in Iberian pigs using genome-wide data. Genet Sel Evol. 2015;47:1.
15. Pryce JE, Haile-Mariam M, Goddard ME, Hayes BJ. Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle. Genet Sel Evol. 2014;46:71.
16. Gulisija D, Gianola D, Weigel KA. Nonparametric analysis of the impact of inbreeding on production in Jersey cows. J Dairy Sci. 2007;90:493-500.
17. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer; 2009.
18. Yao C, Spurlock DM, Armentano LE, Page Jr CD, VandeHaar MJ, Bickhart DM, et al. Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle. J Dairy Sci. 2013;96:6716-29.
19. Howard JT, Maltecca C, Haile-Mariam M, Hayes BJ, Pryce JE. Characterizing homozygosity across United States, New Zealand and Australian Jersey cow and bull populations. BMC Genomics. 2015;16:187.
20. Kim EJ, Cole B, Huson H, Wiggans GR, Van Tassell CP, Crooker BA, et al. Effect of artificial selection on runs of homozygosity in U.S. Holstein cattle. PLoS One. 2013;8(11), e80813.
21. Stachowicz K, Sargolzaei M, Miglior F, Schenkel FS. Rates of inbreeding and genetic diversity in Canadian Holstein and Jersey cattle. J Dairy Sci. 2011;94:5160-75.
22. Haile-Mariam M, Bowman PJ, Goddard ME. A practical approach for minimizing inbreeding and maximizing genetic gain in dairy cattle. Genet Sel Evol. 2007;39:369-89.
23. Doerge RW, Churchill GA. Permutation tests for multiple loci affecting a quantitative character. Genetics. 1996;142:285-94.
24. Park T, Casella G. The Bayesian lasso. J Am Stat Assoc. 2008;103:681-6.
25. Interbull: Interbull routine genetic evaluation for dairy production traits, April 2014. http://www.interbull.org/web/static/mace:evaluations_archive/eval/prod-apr14.html. Accessed July. 19, 2014.
26. Gulisija D, Crow JF. Inferring purging from pedigree data. Evolution. 2007;61:1043-51.
27. Crow JF, Kimura M. An Introduction to Population Genetics Theory. Caldwell, NJ: Harper and Row 1970. Reprinted, 1977, Burgess Pub. Co. Reprinted 2009, Blackurn Press; 2009.
28. Stella A, Ajmone-Marsan P, Lazzari B, Boettcher P. Identification of selection signatures in cattle breeds selected for dairy production. Genetics. 2010;185:1451-61.
29. Porto-Neto LR, Sonstegard TS, Liu GE, Bickhart DM, Da Silva MV, Machado MA, et al. Genomic divergence of zebu and taurine cattle identified through high-density SNP genotyping. BMC Genomics. 2013;14:876.
30. Kemper KE, Saxton SJ, Bolormaa S, Hayes BJ, Goddard ME. Selection for complex traits leaves little or no classic signatures of selection. BMC Genomics. 2014;15:241.
31. Zavarez LB, Utsunomiya YT, Carmo AS, Neves HH, Carvalheiro R, Ferenčaković M, et al. Assessment of autozygosity in Nellore cows (Bos indicus) through high-density SNP genotypes. Front Genet. 2015;6:5.
32. Qanbari S, Pausch H, Jansen S, Somel M, Strom TM, Fries R, et al. Classic selective sweeps revealed by massive sequencing in cattle. PLoS Genet. 2014;10(3):e1004148.
33. Blott S, Kim JJ, Moisio S, Schmidt-Küntzel A, Cornet A, Berzo P, et al. Molecular dissection of a quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition. Genetics. 2003;163:253-66.
34. de Cara MAR, Fernádez J, Toro MA, Villanueva B. Using genome-wide information to minimize the loss of diversity in conservation programmes. J Anim Breed Genet. 2011;128:456-64.
35. Engelsma KA, Veerkamp RF, Calus MPL, Windig JJ. Consequences for diversity when prioritizing animals for conservation with pedigree or genomic information. J Anim Breed Genet. 2011;128:473-81.
36. Pryce JE, Hayes BJ, Goddard ME. Novel strategies to minimize progeny inbreeding while maximizing genetic gain using genomic information. J Dairy Sci. 2012;95:377-88.
37. Sonesson AK, Woolliams JA, Meuwissen TH. Genomic selection requires genomic control of inbreeding. Genet Sel Evol. 2012;44:27.
38. Clark SA, Kinghorn BP, Hickey JM, van der Werf JHJ. The effect of genomic information on optimal contribution selection in livestock breeding programs. Genet Sel Evol. 2013;45:44.
39. Gilmour AR, Gogel BJ, Cullis BR, Thompson R. ASReml User Guide Release 3.0. Hemel Hempstead, UK: VSN International Ltd.; 2009.
40. Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing data inference for whole genome association studies using localized haplotype clustering. Am J Hum Genet. 2007;81:1084-97.
41. Ferenčaković M, Sölkner J, Curik I. Estimating autozygosity from high-throughput information: effect of SNP density and genotyping errors. Genet Sel Evol. 2013;45:42.
42. Henderson CR. A simple method for computing the inverse of a numerator relationship matrix used in prediction of breeding values. Biometrics. 1976;32(69):83.
43. Meuwissen THE, Luo Z. Computing inbreeding coefficients in large populations. Genet Sel Evol. 1992;24:305-13.
44. Garrick DJ, Taylor JF, Fernando RL. Deregressing estimated breeding values and weighting information for genomic regression analyses. Genet Sel Evol. 2009;41:55.
45. Friedman J. Greedy boosting approximation: a gradient boosting machine. Ann Stat. 2001;29:1189-232.
46. Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot. 2013;7:21.
47. Lubke GH, Laurin C, Walters R, Eriksson N, Hysi P, Spector TD, et al. Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data. J Data Mining Genomics Proteomics. 2013;4:10.4172/2153-0602.1000143.
48. Ridgeway G. The gbm package: generalized boosted regression models. R package version. 2010;1:6-3.1.
49. Walters R, Laurin C, Lubke GH. An integrated approach to reduce the impact of minor allele frequency and linkage disequilibrium on variable importance measures for genome-wide data. Bioinformatics. 2012;28:2615-23.
50. de Los CG, Pérez P, Vazquez AI, Crossa J. Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package. Methods Mol Biol. 2013;1019:299-320.
51. Plummer M, Cowles N, Vines K. CODA: convergence diagnosis and output analysis for MCMC. R News. 2006;6:7-11.
52. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38(Suppl):W214-20.